City Data and 311 Response Time

This is my first official blog post! I’ve created this in order to highlight side projects.

In February I completed a full-time immersive Data Science program that focused on data analysis, machine learning and modeling in Python. The program’s curriculum consisted of classification, regression, clustering, git, SQL, relational databases, critical thinking, visualization, presentation and reporting skills.

For my final project, I conducted an analysis of New York City’s 311 Data (>8MM rows) to build a model that predicts the length of time it takes to resolve resident complaints. I hosted data in a SQL database, cleaned data using Python, created visualizations using Matplotlib and Tableau. Next, I modeled data using Random Forest Classification to accurately predict resolution time to be shared with residents and public policy influencers.

To see a deck giving a high level overview of this project click here.

Data Cleaning and Exploratory Data Analysis can be viewed here.

Random Forest Classification available here.